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Article ; Online: Standardizing Extracted Data Using Automated Application of Controlled Vocabularies.

Foster, Caroline / Wignall, Jessica / Kovach, Samuel / Choksi, Neepa / Allen, Dave / Trgovcich, Joanne / Rochester, Johanna R / Ceger, Patricia / Daniel, Amber / Hamm, Jon / Truax, Jim / Blake, Bevin / McIntyre, Barry / Sutherland, Vicki / Stout, Matthew D / Kleinstreuer, Nicole

Environmental health perspectives

2024 Volume 132, Issue 2, Page(s) 27006

Abstract: Background: Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. ... ...

Abstract	Background: Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use. Objectives: To minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies. Methods: We created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language. Results: We automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved Discussion: Augmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. https://doi.org/10.1289/EHP13215.
MeSH term(s)	Humans ; Female ; Pregnancy ; Systematic Reviews as Topic ; Vocabulary, Controlled ; Household Articles ; Intelligence ; Research Design
Language	English
Publishing date	2024-02-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	195189-0
ISSN	1552-9924 ; 0091-6765 ; 1078-0475
ISSN (online)	1552-9924
ISSN	0091-6765 ; 1078-0475
DOI	10.1289/EHP13215
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Workshop Report: Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Harmonized Language.

Holmgren, Stephanie / Bell, Shannon M / Wignall, Jessica / Duncan, Christopher G / Kwok, Richard K / Cronk, Ryan / Osborn, Kimberly / Black, Steven / Thessen, Anne / Schmitt, Charles

International journal of environmental research and public health

2023 Volume 20, Issue 3

Abstract: Harmonized language is essential to finding, sharing, and reusing large-scale, complex data. Gaps and barriers prevent the adoption of harmonized language approaches in environmental health sciences (EHS). To address this, the National Institute of ... ...

Abstract	Harmonized language is essential to finding, sharing, and reusing large-scale, complex data. Gaps and barriers prevent the adoption of harmonized language approaches in environmental health sciences (EHS). To address this, the National Institute of Environmental Health Sciences and partners created the Environmental Health Language Collaborative (EHLC). The purpose of EHLC is to facilitate a community-driven effort to advance the development and adoption of harmonized language approaches in EHS. EHLC is a forum to pinpoint language harmonization gaps, to facilitate the development of, raise awareness of, and encourage the use of harmonization approaches and tools, and to develop new standards and recommendations. To ensure that EHLC's focus and structure would be sustainable long-term and meet the needs of the field, EHLC launched an inaugural workshop in September 2021 focused on "Developing Sustainable Language Solutions" and "Building a Sustainable Community". When the attendees were surveyed, 91% said harmonized language solutions would be of high value/benefit, and 60% agreed to continue contributing to EHLC efforts. Based on workshop discussions, future activities will focus on targeted collaborative use-case working groups in addition to offering education and training on ontologies, metadata, and standards, and developing an EHS language resource portal.
MeSH term(s)	United States ; Environmental Health ; Language ; National Institute of Environmental Health Sciences (U.S.)
Language	English
Publishing date	2023-01-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph20032317
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Enabling novel paradigms: a biological questions-based approach to human chemical hazard and drug safety assessment.

Berridge, Brian R / Bucher, John R / Sistare, Frank / Stevens, James L / Chappell, Grace A / Clemons, Meredith / Snow, Samantha / Wignall, Jessica / Shipkowski, Kelly A

Toxicological sciences : an official journal of the Society of Toxicology

2023 Volume 198, Issue 1, Page(s) 4–13

Abstract: Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, ...

Abstract	Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated.
MeSH term(s)	Animals ; Humans ; Risk Assessment/methods ; Animal Use Alternatives
Language	English
Publishing date	2023-12-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1420885-4
ISSN	1096-0929 ; 1096-6080
ISSN (online)	1096-0929
ISSN	1096-6080
DOI	10.1093/toxsci/kfad124
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Novel text analytics approach to identify relevant literature for human health risk assessments: A pilot study with health effects of in utero exposures

Cawley, Michelle / Beardslee, Renee / Beverly, Brandy / Cowden, John / Hotchkiss, Andrew / Kirrane, Ellen / Sams, Reeder / Varghese, Arun / Wignall, Jessica

Environment international. 2020 Jan., v. 134

2020

Abstract: Systematic reviews involve mining literature databases to identify relevant studies. Identifying potentially relevant studies can be informed by computational tools comparing text similarity between candidate studies and selected key (i.e., seed) ... ...

Abstract	Systematic reviews involve mining literature databases to identify relevant studies. Identifying potentially relevant studies can be informed by computational tools comparing text similarity between candidate studies and selected key (i.e., seed) references.ChallengeUsing computational approaches to identify relevant studies for risk assessments is challenging, as these assessments examine multiple chemical effects across lifestages (e.g., human health risk assessments) or specific effects of multiple chemicals (e.g., cumulative risk). The broad scope of potentially relevant literature can make selection of seed references difficult.ApproachWe developed a generalized computational scoping strategy to identify human health relevant studies for multiple chemicals and multiple effects. We used semi-supervised machine learning to prioritize studies to review manually with training data derived from references cited in the hazard identification sections of several US EPA Integrated Risk Information System (IRIS) assessments. These generic training data or seed studies were clustered with the unclassified corpus to group studies based on text similarity. Clusters containing a high proportion of seed studies were prioritized for manual review. Chemical names were removed from seed studies prior to clustering resulting in a generic, chemical-independent method for identifying potentially human health relevant studies. We developed a case study that focused on identifying the array of chemicals that have been studied with respect to in utero exposure to test the recall of this novel literature searching strategy. We then evaluated the general strategy of using generic, chemical-independent training data with two previous IRIS assessments by comparing studies predicted relevant to those used in the assessments (i.e., total relevant).OutcomeA keyword search designed to retrieve studies that examined the in utero effects of environmental chemicals identified over 54,000 candidate references. Clustering algorithms were applied using 1456 studies from multiple IRIS assessments with chemical names removed as training data or seeds (i.e., semi-supervised learning). Using a six-algorithm ensemble approach 2602 articles, or approximately 5% of candidate references, were “voted” relevant by four or more clustering algorithms and manual review confirmed nearly 50% of these studies were relevant. Further evaluations on two IRIS assessments, using a nine-algorithm ensemble approach and a set of generic, chemical-independent, externally-derived seed studies correctly identified 77–83% of hazard identification studies published in the assessments and eliminated the need to manually screen more than 75% of search results on average.LimitationsThe chemical-independent approach used to build the training literature set provides a broad and unbiased picture across a variety of endpoints and environmental exposures but does not systematically identify all available data. Variance between actual and predicted relevant studies will be greater because of the external and non-random origin of seed study selection. This approach depends on access to readily available generic training data that can be used to locate relevant references in an unclassified corpus.ImpactA generic approach to identifying human health relevant studies could be an important first step in literature evaluation for risk assessments. This initial scoping approach could facilitate faster literature evaluation by focusing reviewer efforts, as well as potentially minimize reviewer bias in selection of key studies. Using externally-derived training data has applicability particularly for databases with very low search precision where identifying training data may be cost-prohibitive.
Keywords	algorithms ; artificial intelligence ; case studies ; cumulative risk ; databases ; hazard identification ; health effects assessments ; human health ; maternal exposure ; seeds ; systematic review ; variance
Language	English
Dates of publication	2020-01
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2019.105228
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses.

Watford, Sean / Ly Pham, Ly / Wignall, Jessica / Shin, Robert / Martin, Matthew T / Friedman, Katie Paul

Reproductive toxicology (Elmsford, N.Y.)

2019 Volume 89, Page(s) 145–158

Abstract: The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public ... ...

Abstract	The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
MeSH term(s)	Animals ; Computational Biology/methods ; Computational Biology/trends ; Databases, Factual/trends ; Dose-Response Relationship, Drug ; Hazardous Substances/chemistry ; Hazardous Substances/classification ; Hazardous Substances/toxicity ; Models, Biological ; Software ; Toxicology/methods ; Toxicology/trends ; United States ; United States Environmental Protection Agency
Chemical Substances	Hazardous Substances
Language	English
Publishing date	2019-07-21
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	639342-1
ISSN	1873-1708 ; 0890-6238
ISSN (online)	1873-1708
ISSN	0890-6238
DOI	10.1016/j.reprotox.2019.07.012
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software.

Pham, Ly Ly / Watford, Sean / Friedman, Katie Paul / Wignall, Jessica / Shapiro, Andrew J

Reproductive toxicology (Elmsford, N.Y.)

2019 Volume 90, Page(s) 102–108

Abstract: Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency's ... ...

Abstract	Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency's Toxicity Reference Database (ToxRefDB v2.0), the Health Assessment Workspace Collaborative (HAWC), and the National Toxicology Program's Chemical Program's Chemical Effects in Biological Systems (CEBS). These resources provide effect level information but modeling these data to a curve may be more informative for predictive toxicology applications. Benchmark Dose Software (BMDS) has been recognized broadly and used for regulatory applications at multiple agencies. However, the current BMDS software was not amenable to modeling large datasets. Herein we describe development and use of a Python package that implements a wrapper around BMDS, a software that requires manual input in the dose-response modeling process (i.e., best-fitting model-selection, reporting, and dose-dropping). In the Python BMDS, users can select the BMDS version, customize model recommendation logic, and export summaries of the resultant BMDS output. Further, using the Python interface, a web-based application programming interface (API) has been developed for easy integration into other software systems, pipelines, or databases. Software utility was demonstrated via modeling nearly 28,000 datasets in ToxRefDB v2.0, re-creation of an existing, published large-scale analysis, and demonstration of usage in software such as CEBS and HAWC. Python BMDS enables rapid-batch processing of dose-response datasets using a modeling software with broad acceptance in the toxicology community, thereby providing an important tool for leveraging the publicly available quantitative toxicology data in a reproducible manner.
MeSH term(s)	Dose-Response Relationship, Drug ; Humans ; Internet ; Libraries, Digital ; Models, Biological ; Risk Assessment ; Software ; United States ; United States Environmental Protection Agency
Language	English
Publishing date	2019-08-12
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	639342-1
ISSN	1873-1708 ; 0890-6238
ISSN (online)	1873-1708
ISSN	0890-6238
DOI	10.1016/j.reprotox.2019.07.013
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating potential human health risks from modeled inhalation exposures to volatile organic compounds emitted from oil and gas operations.

Holder, Chris / Hader, John / Avanasi, Raga / Hong, Tao / Carr, Ed / Mendez, Bill / Wignall, Jessica / Glen, Graham / Guelden, Belle / Wei, Yihua

Journal of the Air & Waste Management Association (1995)

2019 Volume 69, Issue 12, Page(s) 1503–1524

Abstract: Some states and localities restrict siting of new oil and gas (O&G) wells relative to public areas. Colorado includes a 500-foot exception zone for building units, but it is unclear if that sufficiently protects public health from air emissions from O&G ... ...

Abstract	Some states and localities restrict siting of new oil and gas (O&G) wells relative to public areas. Colorado includes a 500-foot exception zone for building units, but it is unclear if that sufficiently protects public health from air emissions from O&G operations. To support reviews of setback requirements, this research examines potential health risks from volatile organic compounds (VOCs) released during O&G operations.We used stochastic dispersion modeling with published emissions for 47 VOCs (collected on-site during tracer experiments) to estimate outdoor air concentrations within 2,000 feet of hypothetical individual O&G facilities in Colorado. We estimated distributions of incremental acute, subchronic, and chronic inhalation non-cancer hazard quotients (HQs) and hazard indices (HIs), and inhalation lifetime cancer risks for benzene, by coupling modeled concentrations with microenvironmental penetration factors, human-activity diaries, and health-criteria levels.Estimated exposures to most VOCs were below health criteria at 500-2,000 feet. HQs were < 1 for 43 VOCs at 500 feet from facilities, with lowest values for chronic exposures during O&G production. Hazard estimates were highest for acute exposures during O&G development, with maximum acute HQs and HIs > 1 at most distances from facilities, particularly for exposures to benzene, 2- and 3-ethyltoluene, and toluene, and for hematological, neurotoxicity, and respiratory effects. Maximum acute HQs and HIs were > 10 for highest-exposed individuals 500 feet from eight of nine modeled facilities during O&G development (and 2,000 feet from one facility during O&G flowback); hematologic toxicity associated with benzene exposure was the critical toxic effect. Estimated cancer risks from benzene exposure were < 1.0 × 10
MeSH term(s)	Air Pollutants/chemistry ; Air Pollutants/toxicity ; Colorado ; Environmental Monitoring/methods ; Humans ; Industrial Waste ; Inhalation Exposure/analysis ; Models, Biological ; Oil and Gas Industry ; Volatile Organic Compounds/chemistry
Chemical Substances	Air Pollutants ; Industrial Waste ; Volatile Organic Compounds
Language	English
Publishing date	2019-11-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1003064-5
ISSN	2162-2906 ; 0894-0630 ; 1047-3289 ; 1096-2247
ISSN (online)	2162-2906
ISSN	0894-0630 ; 1047-3289 ; 1096-2247
DOI	10.1080/10962247.2019.1680459
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Epithelial ovarian cancer is infiltrated by activated effector T cells co-expressing CD39, PD-1, TIM-3, CD137 and interacting with cancer cells and myeloid cells.

Tassi, Elena / Bergamini, Alice / Wignall, Jessica / Sant'Angelo, Miriam / Brunetto, Emanuela / Balestrieri, Chiara / Redegalli, Miriam / Potenza, Alessia / Abbati, Danilo / Manfredi, Francesco / Cangi, Maria Giulia / Magliacane, Gilda / Scalisi, Fabiola / Ruggiero, Eliana / Maffia, Maria Chiara / Trippitelli, Federica / Rabaiotti, Emanuela / Cioffi, Raffaella / Bocciolone, Luca /

Candotti, Giorgio / Candiani, Massimo / Taccagni, Gianluca / Schultes, Birgit / Doglioni, Claudio / Mangili, Giorgia / Bonini, Chiara

Frontiers in immunology

2023 Volume 14, Page(s) 1212444

Abstract: Introduction: Despite predicted efficacy, immunotherapy in epithelial ovarian cancer (EOC) has limited clinical benefit and the prognosis of patients remains poor. There is thus a strong need for better identifying local immune dynamics and immune- ... ...

Abstract	Introduction: Despite predicted efficacy, immunotherapy in epithelial ovarian cancer (EOC) has limited clinical benefit and the prognosis of patients remains poor. There is thus a strong need for better identifying local immune dynamics and immune-suppressive pathways limiting T-cell mediated anti-tumor immunity. Methods: In this observational study we analyzed by immunohistochemistry, gene expression profiling and flow cytometry the antigenic landscape and immune composition of 48 EOC specimens, with a focus on tumor-infiltrating lymphocytes (TILs). Results: Activated T cells showing features of partial exhaustion with a CD137 Conclusion: These data demonstrate that EOC is enriched in CD137
MeSH term(s)	Humans ; Female ; T-Lymphocytes ; Hepatitis A Virus Cellular Receptor 2/metabolism ; Programmed Cell Death 1 Receptor/metabolism ; Carcinoma, Ovarian Epithelial/metabolism ; Leukocyte Common Antigens/metabolism ; Ovarian Neoplasms ; Myeloid Cells/metabolism ; Tumor Microenvironment
Chemical Substances	Hepatitis A Virus Cellular Receptor 2 ; Programmed Cell Death 1 Receptor ; Leukocyte Common Antigens (EC 3.1.3.48)
Language	English
Publishing date	2023-10-04
Publishing country	Switzerland
Document type	Observational Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1212444
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Novel text analytics approach to identify relevant literature for human health risk assessments: A pilot study with health effects of in utero exposures.

Cawley, Michelle / Beardslee, Renee / Beverly, Brandy / Hotchkiss, Andrew / Kirrane, Ellen / Sams, Reeder / Varghese, Arun / Wignall, Jessica / Cowden, John

Environment international

2019 Volume 134, Page(s) 105228

Abstract: Background: Systematic reviews involve mining literature databases to identify relevant studies. Identifying potentially relevant studies can be informed by computational tools comparing text similarity between candidate studies and selected key (i.e., ... ...

Abstract	Background: Systematic reviews involve mining literature databases to identify relevant studies. Identifying potentially relevant studies can be informed by computational tools comparing text similarity between candidate studies and selected key (i.e., seed) references. Challenge Using computational approaches to identify relevant studies for risk assessments is challenging, as these assessments examine multiple chemical effects across lifestages (e.g., human health risk assessments) or specific effects of multiple chemicals (e.g., cumulative risk). The broad scope of potentially relevant literature can make selection of seed references difficult. Approach We developed a generalized computational scoping strategy to identify human health relevant studies for multiple chemicals and multiple effects. We used semi-supervised machine learning to prioritize studies to review manually with training data derived from references cited in the hazard identification sections of several US EPA Integrated Risk Information System (IRIS) assessments. These generic training data or seed studies were clustered with the unclassified corpus to group studies based on text similarity. Clusters containing a high proportion of seed studies were prioritized for manual review. Chemical names were removed from seed studies prior to clustering resulting in a generic, chemical-independent method for identifying potentially human health relevant studies. We developed a case study that focused on identifying the array of chemicals that have been studied with respect to in utero exposure to test the recall of this novel literature searching strategy. We then evaluated the general strategy of using generic, chemical-independent training data with two previous IRIS assessments by comparing studies predicted relevant to those used in the assessments (i.e., total relevant). Outcome A keyword search designed to retrieve studies that examined the in utero effects of environmental chemicals identified over 54,000 candidate references. Clustering algorithms were applied using 1456 studies from multiple IRIS assessments with chemical names removed as training data or seeds (i.e., semi-supervised learning). Using a six-algorithm ensemble approach 2602 articles, or approximately 5% of candidate references, were "voted" relevant by four or more clustering algorithms and manual review confirmed nearly 50% of these studies were relevant. Further evaluations on two IRIS assessments, using a nine-algorithm ensemble approach and a set of generic, chemical-independent, externally-derived seed studies correctly identified 77-83% of hazard identification studies published in the assessments and eliminated the need to manually screen more than 75% of search results on average. Limitations The chemical-independent approach used to build the training literature set provides a broad and unbiased picture across a variety of endpoints and environmental exposures but does not systematically identify all available data. Variance between actual and predicted relevant studies will be greater because of the external and non-random origin of seed study selection. This approach depends on access to readily available generic training data that can be used to locate relevant references in an unclassified corpus. Impact A generic approach to identifying human health relevant studies could be an important first step in literature evaluation for risk assessments. This initial scoping approach could facilitate faster literature evaluation by focusing reviewer efforts, as well as potentially minimize reviewer bias in selection of key studies. Using externally-derived training data has applicability particularly for databases with very low search precision where identifying training data may be cost-prohibitive.
MeSH term(s)	Algorithms ; Environmental Exposure ; Humans ; Pilot Projects ; Risk Assessment ; United States ; United States Environmental Protection Agency
Language	English
Publishing date	2019-11-08
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2019.105228
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Conditional Toxicity Value (CTV) Predictor: An

Wignall, Jessica A / Muratov, Eugene / Sedykh, Alexander / Guyton, Kathryn Z / Tropsha, Alexander / Rusyn, Ivan / Chiu, Weihsueh A

Environmental health perspectives

2018 Volume 126, Issue 5, Page(s) 57008

Abstract: Background: Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be ... ...

Abstract	Background: Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. Objectives: As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. Methods: We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. Results: QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q Conclusions: An
MeSH term(s)	Animals ; Computer Simulation ; Databases, Factual ; Humans ; Quantitative Structure-Activity Relationship ; Risk Assessment/methods
Language	English
Publishing date	2018-05-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	195189-0
ISSN	1552-9924 ; 0091-6765 ; 1078-0475
ISSN (online)	1552-9924
ISSN	0091-6765 ; 1078-0475
DOI	10.1289/EHP2998
Database	MEDical Literature Analysis and Retrieval System OnLINE

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In stock of ZB MED Cologne/Königswinter

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In stock of ZB MED Cologne/Königswinter

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In stock of ZB MED Cologne/Königswinter

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In stock of ZB MED Cologne/Königswinter

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In stock of ZB MED Cologne/Königswinter

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